WPS5831
Policy Research Working Paper 5831
Distributional Impact Analysis
of the Energy Price Reform in Turkey
Fan Zhang
The World Bank
Europe and Central Asia Region
Office of the Chief Economist
October 2011
Policy Research Working Paper 5831
Abstract
A pricing reform in Turkey increased the residential data in survey research. The study reveals a highly skewed
electricity tariff by more than 50 percent in 2008. The distribution of price elasticities in the population, with
reform, aimed at encouraging energy efficiency and rich households three times more responsive in adjusting
private investment, sparked considerable policy debate consumption to price changes than the poor. This is
about its potential impact on household welfare. This most likely because the poor are close to their minimum
paper estimates a short-run residential electricity demand electricity consumption levels and have fewer coping
function for evaluating the distributional consequences options. In addition, the welfare loss of the poorest
of the tariff reform. The model allows heterogeneity quintile—measured by the consumer surplus change
in household price sensitivities and is estimated using as a percentage of income—is 2.9 times of that of the
a national sample of 18,671 Turkish households. The wealthiest.
model also addresses the common problem of missing
This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by
the World Bank to provide open access to its research and make a contribution to development policy discussions around
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contacted at fzhang1@worldbank.org.
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Distributional Impact Analysis of the Energy Price Reform in Turkey
Fan Zhang1
JEL Classification: D12, D60, Q41, Q48
Key Words: Energy price reform, distributional impact, Turkey
1
Office of the Chief Economist, Europe and Central Asia
I. Introduction
With growing concerns over energy security and climate change, policy-makers have
increasingly come to realize that energy prices will have to rise in order to reflect the full cost of
consumption. In developing countries, this involves removing subsidies to bring energy prices
closer to market rates; in the developed world, the goal is to internalize environmental costs into
energy prices. However, at the same time, there is concern that higher energy prices will create
economic distress, particularly for the poor households.
Recent reforms in Turkey illustrate these issues. Turkish residential electricity tariffs remained
constant during 2002-07, even though fuel costs increased. In 2008, a cost-based pricing
mechanism was approved, raising the retail tariff by about 50 percent over one year. Pricing
reform is one of the key measures of the electricity market reform launched in Turkey in 2001. It
is considered essential for encouraging energy efficiency, attracting private investment, and
improving the financial position of the state owned electric utilities. Nonetheless, the magnitude
of the price increase was unprecedented. How it would affect households’ consumption and
welfare has prompted public dialogue and policy debate.
The purpose of this paper is to present a partial equilibrium analysis of the welfare impact of the
tariff reform in Turkey.2 I estimate a short-run residential demand function for electricity which
can be used to quantify the welfare effect of price increases on different income groups. By
allowing price elasticities to vary with household income (in the cross section), the model reveals
a highly differentiated distribution of demand elasticities in the population, with wealthy
households three times more responsive in adjusting consumption to price changes than the
poorest, ceteris paribus.
This result has noteworthy welfare implications. Because poor households are less adaptable to
rising tariffs, they suffer a disproportionately larger welfare loss. Although this issue has
received attention in the literature, few studies have addressed it in a systematic way.3 The model
also indicates a smaller aggregate price elasticity of demand than a model ignoring the
heterogeneity in households’ price elasticities, suggesting that a homogeneous estimator will
both neglect the variance and underestimate the aggregate impact on the population.
I estimate the demand model using disaggregated consumption data for a representative sample
of 18,671 Turkish households from the 2008 and 2009 Household Budget Survey (HBS) of the
Turkish Statistical Institute. The dataset provides rich detail in appliance ownership, dwelling
2
It should be noted that although electricity reform may result in price increases, it also provides opportunities
that would not otherwise exist to improve quality and reliability of power supply, and to re-direct public resources
more transparently to the poor. Analyzing the broader impact of electricity pricing reform is beyond the scope of
the paper.
3
Previous studies have noted that poor households devote a larger proportion of income to energy, and
therefore, carry a disproportionally higher burden of rising energy prices. However, few (if any) studies have
estimated the differences in the welfare loss (measured by changes in consumers’ utility) between the poor and non-
poor.
2
characteristics, income and demographic information. The large number of observations
combined with substantial rate changes under the price reform help identify both price and non-
price segments of the demand function.
A common issue of using survey data for statistical analysis is that we only selectively observe
those who choose to report. In the Turkish household budget survey, about 28 percent of
households with access to electricity did not report electricity expenditure. To address potential
sample selection bias, I apply Heckman’s selection model (Heckman, 1979) but do not find
evidence of selection on unobservables, which suggests that sample selection bias is not a
concern in this study. I then use the model to estimate the distributional impact of the 2008 tariff
increase in Turkey. To lend credence to the specification and results, I also conduct out-of-
sample tests of the model that show how well it predicts consumption responses to new price
changes in 2009.
Overall, I find that a 10 percent increase in electricity price will cause an average household in
the bottom income quintile to reduce electricity consumption by 1.8 percent, and an average
household in the top income quintile to reduce electricity consumption by 5 percent, ceteris
paribus. The poor are less flexible in adjusting electricity consumption most likely because they
are close to their minimum electricity consumption level and have fewer options for switching to
other types of fuel.
Not surprisingly, lower income households experienced a greater welfare loss as a percentage of
income. The welfare loss from the 2008 price increases, approximated by consumer surplus
change, is about 164 Turkish Lira (TL) for a bottom income quintile household, or 2.16 percent
of household disposable income, compared with 330 TL, or 0.75 percent of income for the top
quintile. However, because expenditures on electricity have been a moderate component of
household budget in Turkey - they represented 3.5 and 4 percent of the household’s disposable
income in 2008 and 2009 - the impact of the 2008 tariff increase on consumer welfare has been
limited. These results are robust to alternative model specifications, modeling techniques and
sample data.
The rest of the paper is structured as follows. Section II provides the background of Turkish
electricity price reform and a literature review. Section III discusses the empirical strategy.
Section IV describes data and descriptive analysis. Section V presents the estimated price
elasticities, consumer welfare change, and out-of-sample validation tests. Section VI concludes.
II. Background and Literature Review
Turkish Electricity Market Reform
Since 2001, the Turkish Government has embarked on a comprehensive electricity reform
program. The reform aims to establish a competitive electricity market so as to increase private
investment, improve the supply- and demand-side efficiency and ultimately strengthen Turkey’s
3
energy security. Under the reform, the originally vertically-integrated state owned electricity
utility had been split into separate generation, transmission, distribution, and trading companies.
It also established an independent regulatory agency which regulates the sector and oversees the
price. In 2006, a competitive wholesale electricity market was introduced, and in 2008 the
government started to privatize the distribution company.
Despite the progress in the reform, a potentially significant energy supply shortage had been
envisaged, partially due to the lack of progress in electricity tariff reform. During 2002 - 07, the
retail tariff had remained constant, though the price of inputs in electricity production, notably
the natural gas prices, had significantly increased. The disconnect between prices and costs
contributed to the declining supply margin because insufficient tariffs limited funding available
for the maintenance of existing infrastructure and for new investments; additionally, demand
may also have increased at a greater pace without appropriate price signals; finally, a cost
reflective tariff is considered necessary for the continued privatization of electricity distribution.
To address these concerns, the government started pushing for full cost recovery in the electricity
sector in 2008. In January, the electricity price was increased by 20 percent from the fixed level
in previous five years. In March, the government approved a cost-based pricing mechanism,
enabling automatic quarterly tariff adjustments to cover changes in costs incurred by electricity
supply. The new pricing mechanism became effective in July 2008, resulting in another 24
percent price increase in July, and a 9 percent price increase in October. In October 2009, the
government announced another 10- percent price hike from the previous month. Figure 1
illustrates the percentage change in electricity tariff during 2008 – 09. All prices are in 2008 TL.
The price reform is supported by an interest to improve energy security. However, how new
pricing mechanisms would affect households’ consumption and welfare is less known. This
uncertainty has provoked debate in the regulatory policy arena, and has drawn considerable
attention in the market reform. The paper seeks to shed light on the issue.4
Literature Review
How do higher energy prices affect consumer welfare? The literature has focused on both direct
and indirect mechanisms by which consumption may be affected by energy price changes.
Directly, higher energy prices are expected to reduce discretionary income, as consumers have
less money to spend after paying their energy bills. Consumers may also increase their
precautionary savings and delay or forgo purchase of energy-using durables. These effects imply
a reduction in aggregate demand in response to an energy price increase. Indirectly, higher
energy prices are likely to cause reallocation of capital and labor away from energy intensive
industries as consumers switch toward more energy efficient durables. In the presence of
4
An earlier version of this paper on the poverty and social impact analysis of the Turkish electricity tariff
reform has served as an input to the World Bank Development Policy Lending to Turkey, one pillar of which is to
support the government’s efforts on energy market reform.
4
frictions in capital and labor markets, these sectoral shifts will cause resources to be unemployed,
thus causing further cutbacks in consumption. See Kilian (2008) for a review of the direct and
indirect effects.
This paper focuses on a partial equilibrium analysis of the short-term welfare effects of higher
energy prices, while ignoring the spillovers to income, employment and demand in other sectors.
To measure partial welfare effects of price changes, economists typically estimate demand
functions to calculate consumer welfare, a method first introduced by Hausman (1981) and
Vartia (1983). In the setting of this paper, the key question, therefore, is to estimate a short-term
demand function for electricity.
There are two strands of literature using econometric methods to estimate residential electricity
demand. Traditional demand analysis usually assumes homogenous price elasticities (especially
those using data aggregated over time and regions) and model electricity consumption as a
function of income, price and household characteristics.5 See Taylor (1975), Hartman (1979) and
Bohi and Zimmerman (1984) for reviews of these studies.
A recent strand of literature estimates household electricity demand conditional on appliance
ownership and their relations with household characteristics (Parti and Parti, 1980; Hartman and
Werth, 1981; Sebold and Parris, 1989; Bauwens et al. 1994). In a conditional demand framework,
electricity consumption is disaggregated into consumption associated with different appliances
held by the household. This specification measures appliance-specific income and price
elasticities and therefore allows price sensitivities to differ among households with different
appliance portfolio. Swan and Ugursal (2009) provide a review of this approach.
From the point of view of welfare analysis, it is important to understand the distributional effect
of price changes, especially the impact of higher prices on poor and vulnerable households.
Previous studies have shown that the poor are likely to experience higher economic distress
because they spend a larger share of income on energy (World Bank, 2004 and 2007). In fact,
poor households may also be less flexible to adjust electricity consumption facing higher prices
because their consumption is used for basic needs and they have fewer options for switching to
other types of fuel.
To assess differentiated welfare effects, we can estimate a standard demand model separately for
different income groups, such as Nesbakken (1999)6, although doing so sacrifices sample size.
On the other hand, based on conditional demand analyses, we can estimate appliance-specific
5
An active literature on electricity demand modeling had existed in 1970s and 1980s in response to electric
utilities and regulators’ needs for electricity demand forecasting and planning.
6
Nesbakken (1999) estimates the same demand function for two sub-groups of households – those whose
income is higher than the average household income and those whose income is lower than the average income. He
finds that the energy price elasticity is higher for high-income households than for low-income households.
5
price elasticities and link the ownership of price elastic (or inelastic) appliances to household
income. One example is Reiss and White (2005). 7
A conditional demand model assumes the difference in households’ price sensitivities is purely
driven by the difference in households’ appliance holdings. In other words, households with the
same appliance portfolio will exhibit the same level of price sensitivity.8 This assumption is
somewhat contentious because price sensitivities are also likely to be affected by the existing
consumption levels of the appliances. For example, when the electricity price increases, rich
households may adjust by restricting space heating or cooling to rooms which are frequently
used; while households who already restrict the usage of electricity space heating or cooling to
the minimum level may have less room to further cut down its consumption.
Different from previous studies, I develop a model that explicitly estimates the income-based
heterogeneity in price elasticities by incorporating interaction terms of price and income.
Conditional on appliance ownership, dwelling and household characteristics, the model estimates
intra-household differences in price sensitivities that are driven by existing consumption
quantities, habits and other psychological forces which may all be related to household income
levels. The empirical strategy is discussed in the following section.
III. Model
Electricity Demand
Electricity is not consumed directly. In the residential sector, demand for electricity is derived
from the demand for services provided by durable electrical appliances. To summarize this
demand behavior, two types of decisions are involved. The first is the decision about whether
and when to buy or replace an electricity-consuming appliance, given its technical and economic
characteristics; and the second decision is about the frequency and intensity of the use of the
appliance. In the short run, consumers adjust the utilization rate of the existing capital-stock in
response to new levels of prices. While in the long run, both utilization behavior and the
composition of household appliance holdings are variable. Therefore, long-run price elasticities
tend to be much larger than short-run elasticities.
The key difference between long-run and short-run demand is whether the appliance stock is
held constant. To estimate short-run demand elasticities, I therefore incorporate household-level
appliance holdings as fixed variables in the demand function and leave appliance replacement
7
Reiss and White (2005) find that electric space heating or air conditioning exhibit much higher electricity
price elasticity than other appliances. After calculating price elasticity for each household conditional on the
appliance stock, they find low-income households have greater price sensitivity than high-income households in
California. They suggest that this is because there is a weak correlation between household income and ownership of
price sensitive appliances, and that it may be the case that households tend to substitute toward more price-inelastic
electricity uses as income rises.
8
Empirically, a conditional demand model estimates the statistical average price elasticity averaged across
households who own the appliance.
6
decisions for future research when enough time has passed by and when those data become
available. Given the stock of electricity-using devices, the demand for electricity is also
determined by the utilization rate of those devices, which is in turn a function of household
income, electricity prices, the possibility to substitute with other fuels, weather conditions,
dwelling attributes, and household demographic characteristics. Assuming a log-linear functional
form, the demand for electricity of household i is thus defined as:
(1)
where Qi is monthly electricity consumption, Pi is the electricity price, and is annual
household disposable income. Electricity price Pi is treated as exogenous because electricity is
sold under a flat rate schedule in Turkey, therefore, marginal price is not affected by the
consumption levels.9
Recognizing that household demand response to price changes may vary across income groups,
the slope parameter of price ( ) is allowed to be different for households in different income
quintiles10. I interact electricity prices with a categorical income variable Ij (j = 2, 3,..., 5) with Ij
equal to 1 if the household is in income quintile (per capita disposable income) j. Coefficients
associated with the interaction terms are of special interests as they measure price elasticities
across income groups. For example, (j = 2, 3, …, 5) measures the effect of one percent
increase in electricity price on the percentage change of monthly electricity consumption of a
household in income quintile j, all else being equal.
Ci is a vector of observable household characteristics including: RURALi, a dummy variable
equal to 1 if the household resides in a rural location, 0 if in an urban location. As noticed in the
literature, differences in the efficiency of the housing stock and climate may result in differences
in rural-urban consumption (Petersen, 1982); HSIZEi is the number of people living in the
household; AREAi measures the floor area of the dwelling; RENTi takes a value 1 if the
household rents the dwelling and 0 otherwise. This dummy variable may pick up unobservable
differences related to housing ownership in energy consuming behavior. GASi, is a dummy
variable equal to 1 if the household has access to natural gas, 0 if not. Because natural gas is
likely to be a substitute for electricity, access to natural gas may create another source of
variation in electricity demand.
9
Simultaneity between price and quantity becomes a concern when electricity is sold under multistep block
pricing schedule, with price being a function of quantity consumed. As consumers sort along the entire block pricing
schedule, electricity price (marginal and average) is endogenously determined by the quantity consumed. In his
survey paper, Taylor (1975) noted the specification and estimation difficulties raised by the non-linearity of the
pricing structure. Barnes, et al (1981), Hauseman, Kinnucan and McFadden (1979), Henson (1984) and Reiss and
White (2005) discussed methods to address the simultaneity issue associated with nonlinear tariff structure.
10
Ideally, we would also allow cross-section heterogeneity in consumer income elasticities. However,
alternative specification incorporating interaction terms of income and categorical variables of income quintile did
not reveal meaningful differences in income elasticities across income groups.
7
Ain indicates the number of the nth appliance owned by the household. Based on prior empirical
research that has studied households’ appliance use decisions (Parti and Parti, 1980; Reiss and
White, 2005), 15 distinct appliances are modeled in the demand equation, including refrigerators,
freezers, televisions, computers, air conditioners, electric space heating, and swimming pools and
so on. See Table 6 for a complete list of variables entering the model.
Appliance ownership (Ain) is also assumed to be exogenous. This is somewhat contentious
because it is possible that factors affecting appliance utilization behavior also affect decisions to
acquire appliances. In the short run, however, the appliance stock is fixed, and existing evidence
suggests that the bias from ignoring the possible endogeneity is likely to be small. See Reiss and
White (2005) and Sebold and Parris (1989) for a discussion.
Previous studies indicate that weather conditions are important predicators of electricity
consumption (Reiss and White, 2003). However, HBS 2008 and 2009 do not reveal household
geographic locations, making it impossible to match weather data with sample households. As an
alternative way to capture the weather effects, I add two dummy variables SUMi and WINi which
take value 1 if the household is interviewed during the summer months (June – August) and
winter month (December – February), respectively. For completeness, I also include month and
year fixed effects that, as it turns out, deplete the explanatory power of price, while changing the
coefficients of demand parameters only slightly. See Section V and Table 3 for details. This
alternative approach, although capturing the seasonality in electricity demand, does not address
regional variation in weather conditions. In Section V, I also take a formal approach to validating
the model using out-of-sample testing.
Finally, is the error term; , and are unknown
parameters to be estimated.
To test for the robustness of the above demand estimation, I also allow household income to
enter the interaction term as a continuous variable:
(2)
Furthermore, households may not have noticed price changes until after receiving the electricity
bills. To account for delayed response to tariff increase, I also estimate the model using the
electricity prices of the previous month. The results are robust to both alternative specifications.
This is discussed in detail in Section V.
Sample Selection
Using disaggregated data to estimate residential electricity demand is desirable as we avoid the
confounding effect of misspecification arising from aggregation bias. (Blundell, 1988; Barnes,
Gillingham, and Hagemann, 1981; George 1980; McFadden and Dubin 1980). However, using
8
disaggregated micro-survey data often suffers from sample selection bias because electricity
expenditures are only selectively observed for those households choosing to report.
Indeed, even though Turkey has near universal access to electricity, 4,051 households (27.7%) in
the dataset did not report electricity expenditure. Furthermore, poorer households were more
likely to have non-positive electricity expenditures (Table 2), suggesting that the sample
selection is not random. Suppose that households are endowed with observable and
unobservable characteristics, sample selection bias would arise if unobservable characteristics
affecting reporting or non-reporting decisions are correlated with unobservable characteristics
affecting consumption behavior. On the other hand, if non-reporting behavior and electricity
consumption are correlated purely through the observables, we can control for this by including
the appropriate observable variables in the demand equation.
I use the Heckman selection model to test for the selection bias. For the first stage regression,
one needs variables that affect the chances of not reporting electricity expenditures but not the
level of electricity consumption (variables that are not in the consumption equation). The
selection variables used are the education level of the household head, whether the household
head had a permanent job during the survey month, and whether the household head had health
insurance. Education of the household head is putatively not included in the electricity demand
estimation, but may plausibly affect household response to survey questions. Similarly, the job
status and the possession of health insurance of the household head may not directly affect
electricity consumption, but may affect the reporting decision. 11
In the results section, I present estimates of Equation (1) using an ordinary least square (OLS)
model where I limit the observations to households who reported electricity consumption. I also
present estimates from the selection model. I found the two models yield nearly identical results.
Furthermore, both Wald and likelihood ratio tests12 suggest that the unobserved variables
influencing the reporting decision are statistically independent of unobserved factors of the
electricity demand. Therefore, sample selection can be ignored conditional on observable
household characteristics.
IV. Data and Descriptive Analysis
Data
The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is
conducted annually by the Turkish Statistical Institute to collect information on household socio-
economic status, living standards, income, and consumption expenditures. The survey is a
11
A United Nations Development Programme (UNDP) field study on four provinces (Istanbul in both sides of
the Bosporus, Cankiri, Kars and Urfa) (Bagdadiogl etc. 2009) suggests that households who did not pay electricity
bills during the survey month due to arrears or illegal connections would not report electricity expenditure to the
HBS.
12
See Yamagata (2004) for a discussion on the performance of different tests for sample selection bias.
9
nationally representative probability sample of 18,671 households in 2008 and 2009. In 2008, the
survey was conducted monthly with 720 and annually with 8,640 sample households between
January 1 – December 31. That is, survey data of the first 720 households were collected in
January. In February, the survey was carried out on a different set of 720 households. This
rotation continued until the end of December. The 2009 HBS followed the same methodology
but the size of the survey was expanded to contain monthly 1,050 and annually 12,600 sample
households. Because each household appears in the dataset only once, the dataset supports a pure
pooled cross-section analysis.
The survey is conducted through in-home interview. Each interviewer recorded the data on
consumption expenditures during eight visits to the household throughout the survey month,
including one visit prior to the survey month, twice during the 1st and 2nd weeks, once during 3rd
and 4th weeks and once following the end of the survey month. Interviewers also inventory the
households’ appliances, physical characteristics of the residence and collect demographic
information. Further details about the HBS data and survey design are available in Turkish
Statistical Institute (2007).
The survey data are then combined with the actual monthly rate schedule. The Turkish electricity
tariff data have two advantages that are fairly unique to the study. First, in the retail sector,
Turkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until
2011. Under such a system, all residential consumers face a uniform flat-rate price schedule. The
simplicity of the rate structure avoids specification difficulties in identifying marginal electricity
prices – a common challenge for demand studies. Second, the tariff reform in 2008 introduced
three price hikes for residential electricity that jointly increased the price by more than 50
percent from the previous year. The price change by virtue of its magnitude and exogeneity to
the household provides a unique opportunity to identify the price elasticities of electricity
demand.
Descriptive Analysis
Table 1 lists the summary statistics of the variables included in the analysis. I divide the sample
into reporting and non-reporting groups based on whether the household has responded to the
inquiry on electricity expenditure. Table 2 reports mean value and mean differences in household
characteristics between the two groups. Households who did not report electricity expenditure
had lower per capita income and are more likely to reside in rural areas. Heads of these
households are less educated and are more likely to not have a permanent job or health insurance
during the survey month. All these differences are statistically significant at 0.01 level.
A narrower way of assessing the distributional impact of electricity tariff reform is to look at its
effect on the affordability of household electricity services. Affordability can be defined as
households’ ability to purchase an adequate level of utility services without suffering undue
financial hardship (World Bank 2009). Affordability is usually measured by the affordability
10
ratio – the share of income or expenditure allocated to a specific good or service. Figure 2
illustrates the affordability ratio of electricity for all income quintiles from 2003 to 2008.
As indicated in Figure 2, electricity is a normal good in the sense that its budget share declines as
household income rises. This is consistent with observations on energy consumption patterns in
other developing countries (World Bank, 2004 and 2009). Additionally, expenditures on
electricity have been a moderate component of the total budget of the Turkish households even
after the significant price increase in 2008. For an average income household, they represented
2.9 percent of the household’s disposable income in 2007 and increased to 3.5 percent in 2008.
For the lowest quintile households, they rose to 6.3 percent in 2008 from 5.4 percent in 2007,
which is still below the 10-percent benchmark affordability level (Lee, 2007).
V. Results
Demand Parameters
Table 3 presents two sets of regression results. The first set presents electricity demand
regressions based on 2008 HBS data (Columns (1) – (5)). The second set of regressions was
performed based on the combined 2008 and 2009 datasets. Comparison of the results suggests
that all parameter estimates enjoy a high degree of stability across the two datasets.
In addition, parameter estimates are largely insensitive to the choice of model. Columns (1) and
(6) report results from estimating determinants of the log of household monthly electricity
consumption identified in Equation (1) via OLS model. Columns (2) and (7) report estimation
from the Heckman selection model (maximum likelihood). Both Wald and Likelihood Ratio tests
strongly support the contention that the unobserved variables influencing the reporting or non-
reporting decision are statistically independent of unobserved factors of the electricity demand.
Table 4 reports the first stage regression of the Heckman selection model. The estimated
correlation coefficient ( is statistically indistinguishable from zero. Therefore, the selection
process can be ignored conditional on observable household characteristics.
Coefficients of the log of price and the log of income measure the price and income elasticities
of electricity demand. These elasticity estimates correspond to the percentage change in a
household’s monthly electricity consumption resulting from a 1-percent increase in electricity
price or annual household disposable income, holding all else fixed. As expected, the
consumption declines in response to price increases and increases when income rises. Estimates
of income elasticity suggest that a 10 percent increase in annual household disposable income
will cause electricity consumption to increase by about 1 percent. Overall, electricity is price
and income inelastic in the short-run in the residential sector of Turkey. Table 8 summarizes
short-run price and income elasticities estimated in other studies and on other countries. The
estimates of income and price elasticities of the study are in the range of elasticities suggested in
the literature.
11
The coefficients of other variables are consistent with expectations. All estimates have the
expected sign and most are statistically significant. The results suggest that many other factors
are important in determining electricity consumption - a doubling of household size leads to
consumption rising by 10 percent. A 5-percent increase in the floor area increases electricity
usage by 1 percent. Other things being equal, rural households had significantly higher electricity
consumption than their urban counterparts, consistent with previous findings (Peterson, 1982).
Similarly, renting rather than owning a residence has a well determined small positive effect for
electricity. Ownership of fridges, freezers, computers, mobile phones, air conditioners,
dishwashers and microwaves all significantly increase electricity consumption.
It is particularly noteworthy that access to natural gas is unimportant. This is not particularly
surprising since substitutability between gas and electricity is fairly limited, especially in the
short run. Interestingly, gas availability turns out to be a strong predictor of households’
reporting electricity consumption in the first stage regression. This may suggest that areas having
access to gas are likely to be associated with stricter enforcement of bill collection. Finally,
conditional on appliance ownership, price and income, the effect of warmer and colder seasons is
not significant.
Heterogeneity in Price Elasticities
Table 3 also reveals considerable and meaningful heterogeneity in households’ price elasticities.
As noted previously, the model permits households’ price elasticities to vary with households’
income quintile by including the price and income interaction terms. The set of price elasticity
parameters ( is jointly different from zero at the 0.05 level in every demand equation.
Figure 3 illustrates the marked differences in price elasticities for households in different income
categories, especially those between the top and bottom income quintiles.
The estimated coefficients on the price and income quintile interaction terms also show that price
elasticity (in absolute term) is positively correlated with household income - a 10 percent
increase in electricity tariff will bring about a 1.8 percent decline in electricity consumption by
the bottom quintile households, but 5 percent decline by the top quintile households, ceteris
paribus, almost three times of that of the lowest quintile. To put these estimates into perspective,
a typical household in the top quintile will cut back its electricity consumption by 95 kWh per
month after a 50 percent increase in electricity price; while a typical household in the bottom
income quintile will only cut back its consumption by 21 kWh.
Figure 4 provides further information about the heterogeneity in households demand elasticities.
The vertical axis of the graph records month-to-month change of electricity consumption (%) of
households in the bottom and top income quintile during 2008. They are residual values from
estimating Equation (1), excluding electricity price and seasonal dummies. The residual values
therefore contain the variability that can be explained by temporal price variation and monthly
fixed effects. As shown in Figure 4, the percentage change in electricity consumption of the top
12
income quintile is in general much larger than that of the bottom income group, especially during
the months of August, October and November.13
As discussed earlier, I also allow price elasticities to vary linearly with the logarithm of
household income which is specified in Equation (2). The corresponding demand estimates are
summarized in Column (5) of Table 3 and Panel A of Table 5. Of particular interest of the results
is that the coefficient of the interaction term between price and income is negative and is
statistically significant at 0.01 level (shown in Table 5 Panel A). This result is consistent with
what is discussed above that the price elasticity of demand (in absolute term) is a positive
function of income. Figure 5 visually describes the relationship between income and price
elasticity. It plots a price elasticity curve fitted to the 10,099 sample households in 2009 against
their corresponding income. As shown in Figure 5, with the increase in income, demand changes
from being perfectly inelastic to perfectly elastic.
To compare the magnitude of price and income elasticities estimated with Equations (1) and (2),
I calculate average price elasticities for each income group at the group mean of household
income based on estimates of Equation (2). In addition, the average income elasticity is
calculated at the mean of the 2008 electricity tariff. The resulting price and income elasticities,
presented in Panel B of Table 5, closely resemble those estimated from Equation (1).
It is also useful to compare the above results with those assuming homogenous price elasticities.
Columns (4) and (9) report the demand determinants if we ignore the differences in households’
price sensitivities along the income distribution. The resulting homogenous estimator of price
elasticity is -0.5. Using survey sampling weights, the average price elasticity estimated from
Equation (1) is -0.3, which is lower than the homogenous estimator. Such a discrepancy suggests
that neglecting heterogeneity in households’ price sensitivity not only ignores the distributional
impact of price increases, but also underestimates the overall impact on the population.
Finally, because Equations (1) and (2) only control for seasonal impacts of weather conditions on
electricity consumption, it is worth comparing these results with estimations that also control for
monthly and yearly fixed effects. Columns (3) and (8) of Table 3 report demand estimates based
on Equation (1) while including monthly and yearly dummies. It is not surprising that the
variation in price is partially depleted by monthly dummies, and the price elasticity cannot be
separately estimated for households in the bottom quintile. However, the pattern between price
elasticity and income persists, and the estimated price elasticities differ systematically along the
rest of the income distribution; their values do not change significantly from those estimated in
other models.
13
During January and February, rich households increased electricity consumption, possibly because rich
households are more likely to use electricity for space heating.
13
Distributional Impact of Tariff Increases
In this section, I examine the welfare impact of electricity price changes on consumers in
different income quintiles based on the estimated demand function. The welfare loss from price
increases can be approximated by the consumer surplus change. For a log linear demand
function, , the consumer surplus change of households in income quintile i is given
by:
(3)
where is the price elasticity; Q1i and P1 are the initial consumption and price, Q2i and P2 are
the new consumption and price. Ai is a constant and is calculated at the means of the exogenous
variables for households in income quintile i. Details on the calculation of consumer surplus
change of a log linear demand function are provided in the Appendix.
Table 7 summarizes the estimated consumer surplus change by income quintile. The annual
welfare loss from 2008 price increases is estimated to be 164 TL for an average bottom income
quintile household, or 2.16 percent of household disposable income, compared with 330 TL, or
0.75 percent of disposable income for the top quintile. Notably, the consumer surplus change is
greater than the financial loss, because it also captures the welfare loss from the reduced
consumption.
The results demonstrate that how price elasticities vary with household income has important
welfare implications. The burden on the poorest quintile, measured by the consumer surplus
change as a percentage of income, is 2.9 times of that of the wealthiest quintile. The welfare
effects of electricity price changes would evidently be disproportionally borne by the low-
income segment of the population distribution.
Nonetheless, the overall welfare impact from the price spikes seems to be limited. This is
because Turkish households spent a relatively small amount of their budget on electricity.
Indeed, according to the World Bank, the share of electricity expenditures in household total
expenditures (excluding expenditures on health, durables and rents) in Turkey was still the eighth
lowest among the European and Central Asian countries after price hikes in 2008.
Model validation – out-of-sample test
As discussed earlier, the empirical model does not capture the impact of regional variation in
weather conditions on electricity demand. To examine the model’s validity, I predict household
demand in 2009 using Equation (1) estimated with 2008 HBS data. Figure 6 and 7 compare the
out-of-sample predictions to actual consumption levels in 2009. In Figure 6, the vertical-axis is
the sum of the log of the monthly consumption; while Figure 7 plots the sum of monthly
electricity consumption. In general, the actual and predicted series are close. Although, as more
clearly shown in Figure 7, the model under-predicts overall consumption but it picks up the trend
14
of change fairly well. Furthermore, the average prediction error is – 37 kWh/month, which is
approximately one-fifth of the sample variance of the consumption. The root mean square error
(RMSE) for the predicted data is 0.548, which is lower than the standard deviation of the
consumption sample. Overall, the model appears to deliver reasonable forecasting performance.
VI. Conclusions
This paper analyzes the distributional impact of the 2008 energy pricing reform in Turkey. To
assess the consumer welfare change, I estimate a short-run demand specification that allows
income-based heterogeneity in household price sensitivities. The model is also useful for
understanding the effects of alternative tariff design on energy efficiency, in predicting future
revenue and energy demand, as well as in understanding the potential impact of a carbon tax.
The results confirm that the price elasticity of demand differs systematically along the income
distribution. The price elasticity of the bottom quintile is -0.18, about a third of the price
elasticity of the top income quintile at -0.54. The results indicate that high-income households,
with decreasing marginal utility of electricity, are more sensitive to energy price changes than
low-income households.
Using consumer surplus change to approximate the welfare impact, I found that the price spikes
in 2008 caused a welfare loss of 164 TL for an average bottom income quintile household, or
2.16 percent of household disposable income, compared with 330 TL, or 0.75 percent of
disposable income for the top quintile. The results demonstrate that while poor households are
less flexible to adapt to price increases, they also experience a proportionately higher amount of
welfare loss.
The results imply that when electricity price elasticity varies across household groups, a uniform
increase in the price of electricity can be quite regressive. Furthermore, from the point of view of
energy efficiency policy, energy savings from removing subsidies to poor households will be less
effective than imposing a tax on high-income households because the latter is more sensitive to
price changes. Finally, given the relatively high affordability ratios for the poorest households,
there is a strong case for carefully crafting social protection policies to ensure that stringent
energy pricing policies do not impose undue hardship on poorest households.
Last, the work presented in this paper illustrates the advantages of using a rich micro dataset to
analyze the energy demand of household sector. There are other developing countries
experiencing similar energy price reform. From a methodological point of view, the availability
of household survey data in other developing countries suggests that similar analyses could be
carried out in settings where differentiated price elasticity estimates are needed to evaluate the
distributional impacts of price increase on households.
15
Appendix Consumer Surplus Change Calculations
This appendix describes the method used to calculate the consumer surplus change reported in
Section V. The equation for a log linear demand function can be written as follows:
Where is the price elasticity and A a constant. Electricity price increases lowers the
consumption of electricity, resulting in a decrease in consumer surplus, which is the difference
between what the consumers are willing to pay and what they actually do pay. Assume that
before the tariff reform, electricity is supplied at price P1 with consumption of Q1. Once price is
raised to P2, consumption lowers to Q2. Consumer surplus is described as the area above the
price and below the demand curve. The decrease in consumer surplus as a result of tariff increase
is therefore the area of A+B in Figure A. This consumer surplus change can be calculated by the
following equation:
=
Figure A Consumer Surplus Change
20
P2
Price
10
A B
P1
C
0
0 Q2 Q1 20 40 60
Quantity
16
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18
Table 1-1 Summary Statistics of Analysis Variables
Mean Std. Dev. Min Max
PRICE (TL/kWh) 0.180 0.018 0.148 0.201
CONSUMPTION (kWh) 293 222 1.29 4677
INCOME (TL) 20945 20630 800 624,231
HSIZE 3.9 1.9 1 23
Summary Statistics on Dwelling Characteristics
AREA(Square meters) 103 31 25 600
RURAL (0/1) 0.31 0.46 0 1
RENT (0/1) 0.24 0.42 0 1
GAS ACCESS (0/1) 0.22 0.41 0 1
Summary Statistics on Appliance Ownership
FRIDGE 1 0.17 0 3
FREEZER 0.08 0.27 0 2
COMPUTER 0.4 0.5 0 5
MPHONE 2.0 1.2 0 9
AIRCONDI 0.16 0.5 0 7
DISHWASHER 0.4 0.5 0 3
MICROWAVE 0.10 0.3 0 2
SAUNA 0.001 0.03 0 1
HEATING 0.02 0.15 0 1
Obs. 18,776
Note: Prices and household incomes are in 2008 TL.
Table 1-2 Per capita annual disposable household income (TL) and monthly electricity
consumption (kWh) by income quintiles
2008 2009
Income Household Electricity Obs. Household Electricity Obs.
Quintile Income Consumption Income Consumption
Bottom 20% 2,960 239 1865 3,068 246 2,205
(933) (182) (1035) (234)
2 5,504 279 1795 5,836 254 2,018
(670) (233) (675) (184)
3 7,913 306 1722 8,267 280 2,005
(730) (224) (760) (199)
4 11,041 326 1613 11,662 291 1,972
(1165) (241) (1292) (203)
Top 20% 22,302 380 1577 25,280 338 1,899
(12564) (276) (18637) (262)
Total 9,947 312 8572 10,834 286 10,099
(8805) (241) (11432) (221)
Note: Standard deviations are in brackets. Income data are not comparable across year without inflation
adjustment. Mean electricity consumption is based on consumption of households who reported
electricity expenditures. Income quintiles corresponding to yearly per capita disposable incomes of: less
than 4345 TL, 4345 – 6649, 6649-9224, 9224-13,260, and more than 13,260 in 2008, and less than 4646,
4646-6992, 6992-9656, 9656-14,321, and more than 14,321 in 2009.
19
Table 2 Household Characteristics by Electricity Expenditure Reporting Status
Mean Differences in Means
Variable Non-Reporting Reporting Non Report - Report
HOUSEHOLD INCOME (TL) 13,999 21,957 -7,958***
(182) (174)
RURAL (0/1) 0.57 0.24 0.33***
(0.49) (0.43)
EDUCATION 3.63 4.80 -1.16***
(2.23) (2.73)
PERMANENT JOB (0/1) 0.26 0.38 -0.13***
(0.44) (0.49)
WITH HEALTH INSURANCE 0.90 0.95 -0.047***
(0/1) (0.29) (0.21)
Obs. 4051 14726
Note: EDUCATION is the educational level of the head of the household and takes the following
values: 1 = illiteracy, 2 = literate without diploma, 3 = primary school, 4=primary education,
5=junior high school, 6=vocational school at junior high school level, 7=high school,
8=vocational school at junior high school level, 9=2-year higher educational institution, 10=4-
year higher educational institution and faculties, 11=master and doctoral. *** indicates the
difference between non-reporting and reporting groups is statistically significant at 1% level.
Standard deviations are reported in parentheses.
20
Table 3 Determinants of Log of Household Electricity Demand (kWh)
1 2 3 4 5 6 7 8 9
OLS MLE OLS OLS OLS OLS MLE OLS OLS
(Selection (month (homogeneo (Eq. [2] (Selection (month- (homogen
Explanatory Model) fixed us price continuou Model) year fixed eous price
Variables effects) elasticity) s income) effects) elasticity
Income Quintile
2008 Data 2008 and 2009 Combined Data
(Per Capita)
-0.180** -0.180** - -0.173* -0.183*
Bottom 20% -
(0.081) (0.081) (0.103) (0.099)
2 -0.194* -0.194* -0.203* -0.277*** -0.276*** -0.283***
(0.105) (0.105) (0.115) (0.075) (0.070) (0.073)
3 -0.322 -0.322 -0.334* -0.484*** -0.248 -0.250 -0.247 -0.461***
LN(PRICE)
(0.206) (0.207) (0.203) (0.096) (0.159) (0.162) (0.159) (0.050)
4 -0.395** -0.395** -0.395** -0.395** -0.397** -0.392**
(0.187) (0.187) (0.183) (0.152) (0.150) (0.153)
Top 20% -0.549** -0.547** -0.554** -0.470** -0.480** -0.461**
(0.231) (0.226) (0.227) (0.180) (0.177) (0.179)
LN(INCOME) 0.103** 0.104** 0.102** 0.135*** -0.292*** 0.105*** 0.110*** 0.105*** 0.132***
(0.043) (0.044) (0.042) (0.015) (0.055) (0.018) (0.017) (0.018) (0.009)
HOUSEHOLD 0.048*** 0.048*** 0.048*** 0.040*** 0.040*** 0.059*** 0.057*** 0.059*** 0.052***
SIZE (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)
Ln(AREA) 0.205*** 0.205*** 0.205*** 0.205*** 0.205*** 0.205*** 0.205*** 0.205*** 0.205***
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
RURAL 0.136*** 0.136*** 0.134*** 0.135*** 0.133*** 0.166*** 0.164*** 0.165*** 0.164***
(0.015) (0.014) (0.014) (0.014) (0.015) (0.022) (0.021) (0.022) (0.022)
RENT 0.046** 0.047** 0.046** 0.044** 0.044** 0.023 0.025 0.024 0.020
(0.016) (0.016) (0.017) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)
FRIDGE 0.174** 0.172** 0.172* 0.178** 0.177** 0.120** 0.123** 0.119** 0.120**
(0.088) (0.087) (0.090) (0.084) (0.087) (0.044) (0.044) (0.045) (0.044)
FREEZER 0.151** 0.151*** 0.148*** 0.152*** 0.153*** 0.127*** 0.123*** 0.124*** 0.127***
(0.027) (0.026) (0.026) (0.027) (0.027) (0.020) (0.021) (0.019) (0.021)
COMPUTER 0.102*** 0.101*** 0.102*** 0.100*** 0.101*** 0.091*** 0.091*** 0.092*** 0.090***
(0.016) (0.016) (0.016) (0.017) (0.017) (0.012) (0.012) (0.011) (0.012)
MPHONE 0.042*** 0.042*** 0.041*** 0.044*** 0.044*** 0.035*** 0.035*** 0.035*** 0.037***
21
(0.010) (0.010) (0.010) (0.010) (0.010) (0.008) (0.008) (0.008) (0.008)
AIRCONDI 0.112*** 0.112*** 0.113*** 0.109*** 0.109*** 0.096*** 0.096*** 0.096*** 0.095***
(0.023) (0.024) (0.022) (0.023) (0.023) (0.015) (0.016) (0.016) (0.015)
DISHWASHER 0.114*** 0.114*** 0.112*** 0.113*** 0.114*** 0.118*** 0.117*** 0.117*** 0.119***
(0.009) (0.009) (0.011) (0.010) (0.011) (0.005) (0.006) (0.006) (0.006)
MICROWAVE 0.052** 0.052** 0.052*** 0.047*** 0.047** 0.065*** 0.065*** 0.064*** 0.061***
(0.014) (0.013) (0.012) (0.015) (0.014) (0.014) (0.013) (0.014) (0.013)
SAUNA 0.429* 0.429* 0.434* 0.434* 0.412* 0.049 -0.045 0.058 0.028
(0.268) (0.266) (0.253) (0.259) (0.251) (0.165) (0.210) (0.162) (0.168)
HEATING 0.362 0.362 0.353 0.360* 0.353 0.388*** 0.390*** 0.391*** 0.387***
(0.205) (0.203) (0.224) (0.212) (0.226) (0.060) (0.059) (0.058) (0.063)
GAS -0.023 -0.023 -0.024 -0.023 -0.024 -0.042 -0.041 -0.043 -0.041
(0.042) (0.041) (0.042) (0.041) (0.042) (0.034) (0.035) (0.034) (0.033)
SUM
WIN
CONST 3.177*** 3.174*** 3.538*** 2.388*** 6.537*** 3.164*** 3.102*** 3.522*** 2.464***
(0.358) (0.352) (0.288) (0.135) (0.411) (0.310) (0.289) (0.120) (0.149)
R2 0.241 0.246 0.238 0.239 0.236 0.240 0.234
Obs. 6924 8571 6924 6924 6924 14620 18776 14620 14620
Note: Columns (1) and (2) report results from estimating determinants of the log of household monthly electricity consumption
identified in equation (1) via OLS and Maximum likelihood model using 2008 HBS data. Columns (3) report the results from
estimating equation (2) via OLS using 2008 HBS data. Columns (4) report the results from estimating equation (1) via OLS but
without permitting heterogeneity in price elasticities across income groups using 2008 HBS data. Columns (5) - (8) report estimation
using 2008 and 2009 HBS data. I cannot reject the null hypothesis that coefficients on price elasticities are the same for the OLS and
MLE estimation in all cases. Columns (9) report estimation of equation (3) via OLS using 2008 HBS data. Additional estimates of
equation (3) are reported in Table 5. Standard errors are reported in parentheses. *** indicates significant at the 1% level; **indicates
significant at the 5% level; * indicates significant at the 10% level.
22
Table 4 First Stage regression for the Heckman Selection Model (ML) - Determinants of
household reporting electricity expenditure
1 2
Variables 2008 and 2009 Data 2008 Data
PERMANENT JOB 0.052 0.002
(0.04) (0.036)
EDUCATION 0.014** 0.008
(0.006) (0.007)
HEALTH INSURANCE 0.229*** 0.257
(0.034) (0.176)
Incomequntile2*LN(PRICE) -0.254*** 0.205
(0.080) (0.231)
Incomequntile3*LN(PRICE) -0.324** -0.005
(0.134) (0.156)
Incomequntile4*LN(PRICE) -0.317*** -0.504**
(0.104) (0.177)
Incomequntile5*LN(PRICE) -0.421*** -0.376*
(0.134) (0.227)
LN(INCOME) 0.182*** 0.239
(0.027) (0.042)
HOUSEHOLD SIZE -0.063*** -0.049***
(0.010) (0.011)
ln(AREA) -0.116*** -0.001***
(0.031) (0.0001)
RURAL -0.529*** -0.468***
(0.062) (0.061)
RENT 0.004 0.030
(0.036) (0.050)
FRIDGE 0.190** 0.221
(0.067) (0.161)
FREEZER -0.055 -0.093
(0.046) (0.065)
COMPUTER 0.079** 0.071***
(0.028) (0.019)
MPHONE 0.014 -0.021
(0.024) (0.036)
AIRCONDI 0.114** 0.193***
(0.041) (0.056)
DISHWASHER 0.082*** 0.100***
(0.016) (0.012)
MICROWAVE -0.085** -0.158***
(0.040) (0.055)
SAUNA 5.960*** 5.580***
(0.134) (0.179)
HEATING 0.055 -0.045
(0.047) (0.065)
GAS 0.228*** 0.266**
(0.056) (0.111)
23
SUMMER 0.0004 0.003
(0.022) (0.017)
WINTER 0.122*** 0.036**
(0.038) (0.015)
CONST -1.203*** -1.894***
(0.345) (0.363)
0.005 0.005
(0.017) (0.011)
Obs. 18776 8571
Note: This table reports estimates for the first-stage selection model for MLE of household
electricity demand using 2008 and 2009 HBS data. The dependent variable is whether a
household has chosen to report electricity expenditure during the survey month (1 = reported).
The selection criteria for observing household electricity expenditures are (1) educational level
of the head of the households, (2) whether she/he had a permanent job during the month, and (3)
whether she/he had health insurance. is the heckman estimation of the correlation between the
unobservables affecting household electricity consumption and the unobservable affecting the
decision to not to report electricity expenditures. Both Wald test and likelihood ratio test cannot
reject the null hypothesis that =0. Standard errors are reported in parentheses. *** indicates
significant at the 1% level; **indicates significant at the 5% level; * indicates significant at the
10% level.
24
Table 5 Price and Income Elasticities estimates of Equation (2)
Panel A Income and Price Parameters estimated from Equation (2)
Variables
LN(INCOME)*LN(PRICE) -0.228***
(0.024)
LN(PRICE) 1.735***
(0.224)
LN(HOUSEHOLDINCOME) -0.292***
(0.055)
Panel B Estimated Price and Income Elasticities
Income Quintile Price Elasticity Income Elasticity
Bottom 20% -0.084
2 -0.225
3 -0.308 -0.105
4 -0.383
Top 20% -0.543
Note: Panel A reports estimated coefficients of the following variables - price, income and the
interaction of price and income - defined in equation (3). Panel B reports estimated price
elasticities of demand for each income quintile at the means of the household income of the
corresponding income quintile, and the income elasticity at the means of electricity price.
Table 6 Additional explanatory variables entering demand model
Mnemonic Variable Description
TV Number of TV Number of TVs at home
DVD_VCD Number of DVD and VCD Number of DVD and VCD at home
JACUZZI Number of Jacuzzis Number of Jacuzzis at home
LAUNDRY Number of washing machine Number of washing machines at home
DRYER Number of dryers Number of dryers at home
CARPETW Number of carpet washing Number of carpet washing machines at
machine home
SECONDD Second Dwelling 1 if household has a second dwelling
25
Table 7 Consumer Surplus Change from 2008 Tariff Increase
Income Financial Loss Consumer Surplus Welfare loss as a %
Quintile (per Change (TL) of Household
capita) Income
Bottom 20% 158 164 2.16
2 161 167 1.32
3 216 231 1.30
4 260 282 1.19
Top 20% 295 330 0.75
Note: Financial loss and consumer surplus change are estimated results for an average household in the
relevant income quintile.
Table 8 Short-run Price and Income Elasticities of Residential Electricity Demand
Price Income Data
Beierlein et al (1981) -0.107 0.015 US national aggregated data
(1967-1977)
Bernard et al. (1996) -0.67 0.14 Quebec household survey
(1986-1989)
Donatos and Mergos -0.28 ~ -0.30 0.53 Greece national aggregated
(1991) consumption data (1961-1986)
Halicioglu (2007) -0.46 0.40 Turkey national aggregated
consumption data (1968-2005)
Nesbakken (1999) -0.5 0.2 Norway households survey
(1993-1995)
Reiss and White -0.39 (Population average) California households survey
(2005)* -0.49 (Income less than $18,000) (1993-1997)
-0.34 ($18,000 to $37,000)
-0.37 ($37,000 to $60,000)
-0.29 (More than $60,000)
Note: * Reiss and White (2005) did not separately estimate household income elasticities.
26
Figure 1 Change in Turkish Residential Electricity Tariff Compared to 2007 (%)
70%
Change in Residential Electricity Tariff from
60%
50%
40%
2007 (%)
30%
20%
10%
0%
Source: Turkish Electricity Distribution Company
Figure 2 Share of Household Disposable Income on Electricity (%) (2003-2009)
10
9
8
7
6 Bottom 20%
5 2
4
3
3
2 4
1 Top 20%
0 Total
Sources: Calculation based on HBS 2003, 04, 05, 06, 07, 08, 09
27
Figure 3 Price elasticities estimates by income quintile
0
-.2
-.4
-.6
-.8
1 2 3 4 5
Income Quintile
95% Confidence Interval mean
Note: Price elasticities are estimated from Equation (1) via OLS model based on 2008 and 2009
combined HBS datasets.
28
Figure 4 Monthly Electricity Consumption Change (%) of the Bottom and Top Income Quintiles
in 2008
0.2
Month-by-month Consumption Change 0.15
0.1
Regression Residual
0.05
Bottom 20%
(%)
0
1 2 3 4 5 6 7 8 9 10 11 12 Top 20%
-0.05
-0.1
-0.15
-0.2
Month (Year 2008)
Note: The y-axis represents the estimated month-to-month changes of electricity consumption (%). They
are residual values for bottom and top income quintiles from estimating demand functions excluding the
electricity price and the month.
29
Figure 5 Price Elasticity and Annual Household Disposable Income (2008)
0
-.5
-1
0 100000 200000 300000
Annual household disposable income (TL)
Note: Price elasticities are calculated based on annual household disposable income and demand
parameter estimates of Equation (2).
30
Figure 6 Actual and predicted sum of the log of monthly electricity consumption in 2009
4500
4000
(kWh)
3500
3000
1 2 3 4 6 6 7 8 9 10 11 12
month
Actual Predicted
Note: The vertical axis describes the sum of the log of the monthly electricity consumption of
sample households in 2009. The solid line corresponds to the actual consumption level. The
dashed line depicts the out-of-sample prediction of 2009 consumption based on Equation (1)
estimated with 2008 data.
31
Figure 7 Actual and predicted sum of monthly electricity consumption in 2009
100 120 140 160 180 200 220 240
60 80
1 2 3 4 5 6 7 8 9 10 11 12
month
Actual Predicted
Note: The vertical axis describes the sum of the monthly electricity consumption of sample
households in 2009. The solid line corresponds to the actual consumption level. The dashed line
depicts the out-of-sample prediction of 2009 consumption based on Equation (1) estimated with
2008 data.
32